Our final Global Intelligence Summit takeaway stems from Day Two's Technology, Analysis and COVID-19 panel:
These breakout sessions had great participation from AIRIP members from a number of industries, to include banking, professional sports & entertainment, pharmaceuticals, and public sector organizations. Many participants explained that their companies and organizations were now operating relatively efficiently in the virtual world. Some were quicker than others to react to the pandemic; those that were better positioned for the move to virtual work had already incorporated technologies into the business operations. Some companies had created policies and procedures to facilitate employees with family care responsibilities, telecommuters, etc. The majority of contributors cited technologies that allowed teams to communicate amongst themselves, and with other business units. One challenge cited was the cost of changing platforms - more from an emotional aspect than a financial one. Technology platforms can be very "sticky" and teams may be reluctant to change once they become familiar with them. Another challenge in the post-pandemic world will be how companies decide to balance collaboration between the real and virtual environments. Some people will like the flexibility of continuing to work from home, while others will want what they view as a productive office environment and separation from their homelife. This will surely be an interesting dilemma that every company and individual will need to work through in the coming years.
The conversations gravitated to discussions on artificial intelligence (AI), machine learning (ML) and other more advanced technologies. Most analysts agreed that their careers were not immediately threatened by AI/ML, rather these technologies would change the way intelligence analysis was performed. Ideally, big data will be better aggregated into a way where analysts can spend more time actually analyzing, opposed to searching and sifting through data. It was noted that there may be a natural cultural/organizational resistance among some to: learn how to use these technologies; fear of programming/coding, fear of being replaced/downsized by technology; and fear of having to switch to a new technology once they learn one. Success stories were shared by some; for example, two participants described how they learned basic Python coding to simplify data sorting. They explained how they committed several (many!) hours to teach themselves an algorithm that now saves an average of 20 minutes per day, every day. Anecdotes like these motivated others to join in and share other examples of how adopting new technologies was benefiting their teams.
Ethics was identified as a necessary item to monitor as AI/ML is further utilized. For example, teams may be required to ensure that big data is not manipulated in a way that could be construed as bias towards particular groups or individuals, or could be viewed as an invasion of privacy by customers, employees, or others. One participant provided an anecdote of how an intelligence team learned that AI/ML had aggregated data and focused on particular groups. The team quickly resolved the issue and deleted the data; it was a valuable lesson learned for future algorithms and analysis.
Thank you to breakout session moderators Mike Mallard and Melissa Zellner!